Performance Analysis of Segmentation Algorithms for the Detection of Breast Cancer

Medical Images Breast Cancer Segmentation Based on K-Means Clustering Algorithm: A Review

“…For this study, they achieved the best result with the highest accuracy in real-time. The study of [26] Three machine learning algorithms, such as the kmeans clustering, active contour model (ACM), and fuzzy c-means clustering algorithm, should be used for comparison. To demonstrate the efficiency of these three segmentation algorithms, the experimental assessment uses different quantitative tests to classify images into benign and malignant classifier with the highest accuracy.…”

“…The essential purpose of pre-processing is to enhance the consistency of the image. It prepares the images to be ready for further the cells structures normal and abnormal cells [26]…”

“…This induces a division of the objects into groups from which the metric to be minimized can be calculated [65][66][67]. There are two known characteristics of the K-means clustering algorithm: one is that it requires a predefined…”

“…Aswathy and Jagannath, [26] Suggested that a comparison of Three machine learning algorithms, the clustering k-means, the active contour model (ACM), and the clustering algorithm fuzzy c-means. To demonstrate the efficiency of these three segmentation algorithms, the experimental assessment uses different quantitative tests.…”

Diagnosis of Breast Cancer by K-Mean Clustering and Otsu Thresholding Segmentation Methods

“…The iteration number with the cluster centroid is affected by the first randomly set cluster centroid (Lin and Ji, 2020). Therefore, it can be fixed to achieve higher performance by identifying the cluster centroid at high baseline data points (Aswathy and Jagannath, 2020). Since K-means clustering is usually applied, the data point { ,{ , ..., x n } is grouped into k clusters.…”